Archive | Weka Machine Learning

Weka has a large number of regression algorithms available on the platform. The large number of machine learning algorithms supported by Weka is one of the biggest benefits of using the platform. In this post you will discover how to use top regression machine learning algorithms in Weka. After reading this post you will know: […]

It is really important to have a performance baseline on your machine learning problem. It will give you a point of reference to which you can compare all other models that you construct. In this post you will discover how to develop a baseline of performance for a machine learning problem using Weka. After reading […]

The problem of predictive modeling is to create models that have good performance making predictions on new unseen data. Therefore it is critically important to use robust techniques to train and evaluate your models on your available training data. The more reliable the estimate of the performance on your model, the further you can push […]

A big benefit of using the Weka platform is the large number of supported machine learning algorithms. The more algorithms that you can try on your problem the more you will learn about your problem and likely closer you will get to discovering the one or few algorithms that perform best. In this post you will […]

Raw machine learning data contains a mixture of attributes, some of which are relevant to making predictions. How do you know which features to use and which to remove? The process of selecting features in your data to model your problem is called feature selection. In this post you will discover how to perform feature selection […]

Data is rarely clean and often you can have corrupt or missing values. It is important to identify, mark and handle missing data when developing machine learning models in order to get the very best performance. In this post you will discover how to handle missing values in your machine learning data using Weka. After […]

Often your raw data for machine learning is not in an ideal form for modeling. You need to prepare or reshape it to meet the expectations of different machine learning algorithms. In this post you will discover two techniques that you can use to transform your machine learning data ready for modeling. After reading this […]

Machine learning algorithms make assumptions about the dataset you are modeling. Often, raw data is comprised of attributes with varying scales. For example, one attribute may be in kilograms and another may be a count. Although not required, you can often get a boost in performance by carefully choosing methods to rescale your data. In […]

It is important to take your time to learn about your data when starting on a new machine learning problem. There are key things that you can look at to very quickly learn more about your dataset, such as descriptive statistics and data visualizations. In this post you will discover how you can learn more […]

It is a good idea to have small well understood datasets when getting started in machine learning and learning a new tool. The Weka machine learning workbench provides a directory of small well understood datasets in the installed directory. In this post you will discover some of these small well understood datasets distributed with Weka, […]